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Adapting the women empowerment in agriculture index to specific country context insights and critiques from field work in India
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This presentation was given by Vidya Vemireddy (TCI), as part of the Annual Scientific Conference hosted by the University of Canberra and co-sponsored by the University of Canberra, the Australian Centre for International Agricultural Research (ACIAR) and CGIAR Collaborative Platform for Gender Research. The event took place on April 2-4, 2019 in Canberra, Australia. Read more: https://www.canberra.edu.au/research/faculty-research-centres/aisc/seeds-of-change and https://gender.cgiar.org/annual-conference-2019/
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Adapting the women empowerment in agriculture index to specific country context insights and critiques from field work in India
- Adapting the Women’s Empowerment in Agriculture Index to specific country context: Insights and critiques from field work in India Seeds of Change Conference: Gender Equality through Agricultural Research for Development April 3, 2019 Soumya Gupta, Vidya Vemireddy, Dhiraj Singh, Prabhu Pingali Cornell University
- Background Photo credit: Will Miller, TCI 2017
- What we know about the WEAI ? • Multi- dimensional, direct measure • Intra- household differences in empowerment • 5 domains of agriculture • Consists of 2 sub- indices • 5 Domains of Empowerment (5DE) • Gender Parity Index • A shorter version – A-WEAI was recently introduced
- WEAI in India • TCI implemented the WEAI in India in 2014 • Account for differences across farming systems • Key drivers of women’s disempowerment: Group membership, credit, workload Absolute contribution of sub-indicators to women’s disempowerment in India (Gupta et al 2017)
- Present limitations of the WEAI Used mainly in the USAID’s Feed the Future zones. • Evidence base is lacking for other South Asian countries Questionnaires used in the initial pilots in Bangladesh, Uganda and Guatemala are identical • It is unlikely that the same set of activities/ assets/ sources of income or credit are common across locations The updated WEAI questionnaires recommend using context- specific examples but not how that might affect index construction • Stop short of addressing any associated changes in adequacy thresholds/ cut-offs if the number of activities is in fact revised
- Our objectives Adaptation of WEAI • context-specific, operational, well- defined indicators of access and ownership. • Changes to adequacy requirements Sensitivity analysis • Modify adequacy threshold from 80% to 60% and 40% Consistency check • Compare results from WEAI_India (adapted AWEAI) to AWEAI
- Methodology
- Primary data: TARINA Baseline Survey, 2017 Technical Assistance and Research for Indian Nutrition & Agriculture (TARINA) Sample: 3600 households, 120 villages, 4 districts Multi- topic individual, household and village surveys
- Adapting the WEAI to an Indian context Context- specific activities Input in production decisions markets, forest produce Group membership in ag- nutrition related SHGs Focused on agriculture alone Control over income Credit decisions for agricultural loans Ownership of agricultural land Modifications to 5 of the 7 AWEAI sub- indicators Change in corresponding adequacy requirement for sub- indicator
- District- level WEAI_India after index adaptation We compute the 5 Domains of Empowerment (5DE) sub- index for women in each district (0-1) Headcount ratio, average inadequacy score, aggregate 5DE score Decompose the 5DE to identify absolute & percentage contribution of sub- indicators to disempowerment Sensitivity analysis Use two alternate thresholds to compute 5DE: adequacy in 40% and 60% sub- indicators Test for differences in headcount ratio, average inadequacy score and district 5DE score Consistency check Compare components of WEAI_India to AWEAI Headcount ratio, average inadequacy score, aggregate 5DE score Data analysis
- Results Photo credit: TCI, 2015
- WEAI_India: at least 80% women are disempowered The main drivers of women’s disempowerment are a membership to agriculture- related SHGs, ownership of land and control over income. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 Munger Maharajganj Kandhamal Kalahandi Bihar U.P. Odisha Districtwisedisempowerementscroe Leisure Agricultural related SHGs Control over use of agricultural income Decision on agricultural credit Ownership of agricultural land Input in agricultural production decision
- Sensitivityresults:as thresholdis relaxed,empowermentlevelsimprove • As the threshold for identifying disempowerment increases there is an increase in district 5DE scores • Relative to the original threshold of 20% for identifying disempowerment, an increase to 40% and 60% results in average district 5DE scores that indicate women are empowered in all four districts. • This improvement in district 5DE scores is due to change in the proportion of disempowered women • Significant decline in the proportion of disempowered women in each of the four districts as thresholds varied. • Magnitude of the decline in the disempowered headcount is greater than the magnitude of increase in the average inadequacy scores across districts.
- Consistency: significantdifferences in 5DE b/w WEAI_India & AWEAI • Relative to WEAI_India, the AWEAI underestimates the proportion of disempowered women in each district. • Across districts, at least 82% of women are disempowered as per WEAI_India vis a vis as low as 24% based on the AWEAI. • Likely being driven by tighter adequacy thresholds in WEAI_India. 71.0 71.0 70.7 70.7 71.2 71.2 87.4 87.4 17.1 99.4 10.1 99.9 12.4 100.0 6.1 100.0 54.7 63.4 45.8 54.7 32.1 39.5 59.3 62.2 65.0 62.3 69.7 62.4 68.6 65.4 87.2 80.0 5.9 20.7 9.9 29.1 1.7 10.0 12.8 43.3 86.0 86.0 81.4 81.4 94.2 94.2 85.4 85.4 0.0 100.0 200.0 300.0 400.0 500.0 WEAI_India Reduced_AWEAI WEAI_India Reduced_AWEAI WEAI_India Reduced_AWEAI WEAI_India Reduced_AWEAI KalahandiKandhamalMaharajganjMunger OdishaU.P.Bihar % women who are empowered in WEAI_India & AWEAI Input Decision Asset holding Credit Decision Income Control Member SHG Leisure
- Discussion & Conclusion
- What did we find? When we attempt to adapt the WEAI to site-specific, well-defined indicators of women’s role in agriculture • We end up with a smaller set of relevant agricultural activities • Often accompanied by tighter adequacy requirements Sensitivity analysis indicates that as the threshold is made ‘loose’, there is an improvement in empowerment status at the district level • Driven by an associated change in the proportion of women who are identified as disempowered. Our consistency checks indicate that there are significant differences in the aggregate 5DE statistics between the existing tool (AWEAI) and our adaptation to India (WEAI_India) in each district
- Relevance Methodological • Need for direct, context and sector-specific measures of women’s empowerment • We show how the WEAI can be adapted to suit contextual and operational requirements • Context- specificity may lead to lack of comparability • Generate empirical base for India • Contribute to technical knowledge base by way of sensitivity and consistency analysis Policy • By adapting the index we know precisely what aspect of the sub-indicators is driving women’s disempowerment in a given location • ensure a targeted and efficient program/policy interventions. • Balance between context- specificity and comparability across locations
- Thank you Photo credit: TCI, 2015
Editor's Notes
- The Women’s Empowerment in Agriculture Index (WEAI) was introduced in 2012 as a multidimensional measure to assess women’s access to resources and ability to make decisions in five domains of agriculture: 1) Production, 2) Resources, 3) Control over income, 4) Leadership and 5) Time. The WEAI was an improvement over previously used measures of women’s empowerment in several ways. For one, it focuses specifically on productive domains in agriculture that become relevant for investigating the role of women’s empowerment in the space of agriculture-nutrition linkages. Second, the fact that the index can be disaggregated allows us to identify not just key drivers of women’s (and men’s) disempowerment, but also the contribution of each of these to overall disempowerment. This can be a useful input when designing context-specific policies to mitigate the gender disparity in decision making over access and control of production and household level resources. Third, unlike measures of education and age that are considered to be proxy/ indirect measures of empowerment, the WEAI sub-indicators are direct measures of empowerment. Four, the WEAI accounts not just for women's empowerment but also the intra-household differences in empowerment levels between women and men.
- This study makes four key contributions. First, we provide an example of how the WEAI can be adapted based on challenges of implementing it as- is in an Indian context. This is in contrast to the way the WEAI has been used so far and is a first step in assessing how malleable the index is to site-specific characteristics. Recent discourse on measuring empowerment highlights the i) the importance of using direct measures of empowerment rather than indirect measures that have been previously used ii) use of context and sector-specific measures (Malhotra, Schuler, and Boender 2002; Richardson 2018). While our adaptation of the index is specific to India some of our adapted indices can be useful to other researchers as well. Second, by constructing the WEAI_India, we generate empirical evidence on the level of women’s empowerment across four locations in the country and test for intra-country variations. Third, by carrying out sensitivity analysis, we contribute to the technical understanding of the WEAI. And finally, by comparing our results to the AWEAI we are able to identify aspects of empowerment that are picked up to different degrees by the two formulations.
- We use primary data from a survey conducted in March-May 2017 as a part of Technical Assistance and Research for Indian Nutrition and Agriculture (TARINA) program in India. The TARINA program, led by the Tata- Cornell Institute for Agriculture and Nutrition (TCI) at Cornell University is a consortium of research and development organizations working on the design and promotion of nutrition-sensitive food systems in India. A total of 3600 women in four districts spread across the states of Uttar Pradesh, Bihar and Orissa were surveyed as a part of the TARINA baseline survey. The districts are: Munger (Bihar), Maharajganj (U.P), Kandhamal (Odisha) and Kalahandi (Odisha) – see figure 1. About 20-30 percent of the labor force was involved in agricultural activities across all the districts. About one-fourth of the farmers are cultivators, and the rest are agricultural laborers. More than 80 per cent of the cultivable area is for staple grains such as rice and wheat. Maharajganj (358 thousand hectares) and Kalahandi (275 thousand hectares) have the maximum area under cultivation, followed by Munger (57 thousand hectares) and Kandhamal (50 thousand hectares).
- The activities included in the input in production sub-indicator in the AWEAI are cash crop farming, food crop farming, livestock raising and fisheries. However, we found that i) fisheries were not a source of livelihood in our location and ii) that there were additional activities households engage in that were more relevant to their agricultural context. Accordingly, for this sub-indicator, we expand the list of activities to include location-specific participation in activities like crop cultivation, technology adoption, and marketing of kitchen garden produce, livestock/ livestock produce and forest produce. For the group membership sub-indicator a woman is considered adequate if she is an SHG member AND the SHG acts as a platform for any one of the following: for doing collective livelihood/ source of free seeds and samplings for homestead gardens/ for access to subsidized custom hiring of implements for agricultural activities/ for receiving education about health, nutrition, education and WASH/ receiving training for agriculture activities, livestock activities and kitchen garden activities. These activities are either related to the provision of technical assistance related to agriculture and allied activities or related to the broader theme of health and nutrition. The criteria for adequacy for group membership changes in our analysis to account for not just membership but also to account for the specific purpose of the SHG. Both agricultural and non- agricultural assets are included in the calculation of the AWEAI’s sub-indicator on asset ownership. We found it challenging to elicit responses related to ownership of most non- agricultural assets like consumer durables that can be considered household- level public goods and therefore for which ‘ownership’ is hard to determine. Even in the subset of agricultural assets, we find that the determination of ‘ownership’ of assets like small and large livestock is difficult. For instance, while women may be involved in the care of livestock, there is no way to distinguish their ‘ownership’ of said livestock from that of others in the household. Therefore we argue that for an Indian context the most relevant asset is ownership of agricultural land, the property rights to which can be determined through a legal title/ lease. In effect then we are tightening the adequacy for this sub-indicator since the asset list is reduced to agricultural land only. Similar to asset ownership, the AWEAI sub-indicator on control over income is calculated based on how much control the respondent feels he or she has over the use of income from a range of activities – both, agricultural and non- agricultural. While, non- agricultural activities are important for measuring empowerment in general, we include only income from agricultural activities such as the sale of crops, livestock, forest produce and income from daily agricultural wage labor. This is done to bring consistency by focusing only on agricultural domains when estimating empowerment levels in agriculture.
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